Latour Chase D, Delgado Megan, Su I-Hsuan, Wiener Catherine, Acheampong Clement O, Poole Charles, Edwards Jessie K, Quinto Kenneth, Stürmer Til, Lund Jennifer L, Li Jie, Lopez Nahleen, Concato John, Funk Michele Jonsson
Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Am J Epidemiol. 2025 Feb 5;194(2):524-535. doi: 10.1093/aje/kwae234.
Understanding the potential for, and direction and magnitude of uncontrolled confounding is critical for generating informative real-world evidence. Many sensitivity analyses are available to assess robustness of study results to residual confounding, but it is unclear how researchers are using these methods. We conducted a systematic review of published active-comparator cohort studies of drugs or biologics to summarize use of sensitivity analyses aimed at assessing uncontrolled confounding from an unmeasured variable. We reviewed articles in 5 medical and 7 epidemiologic journals published between January 1, 2017, and June 30, 2022. We identified 158 active-comparator cohort studies: 76 from medical and 82 from epidemiologic journals. Residual, unmeasured, or uncontrolled confounding was noted as a potential concern in 93% of studies, but only 84 (53%) implemented at least 1 sensitivity analysis to assess uncontrolled confounding from an unmeasured variable. The most common analyses were E-values among medical journal articles (21%) and restriction on measured variables among epidemiologic journal articles (22%). Researchers must rigorously consider the role of residual confounding in their analyses and the best sensitivity analyses for assessing this potential bias. This article is part of a Special Collection on Pharmacoepidemiology.
了解未控制混杂因素的可能性、方向和程度对于生成有用的真实世界证据至关重要。有许多敏感性分析可用于评估研究结果对残余混杂因素的稳健性，但尚不清楚研究人员如何使用这些方法。我们对已发表的药物或生物制品活性对照队列研究进行了系统综述，以总结旨在评估未测量变量导致的未控制混杂因素的敏感性分析的使用情况。我们回顾了2017年1月1日至2022年6月30日期间在5种医学和7种流行病学杂志上发表的文章。我们确定了158项活性对照队列研究：76项来自医学杂志，82项来自流行病学杂志。在93%的研究中，残余、未测量或未控制的混杂因素被视为一个潜在问题，但只有84项（53%）进行了至少1次敏感性分析，以评估未测量变量导致的未控制混杂因素。最常见的分析是医学杂志文章中的E值（21%）和流行病学杂志文章中对测量变量的限制（22%）。研究人员必须在分析中严格考虑残余混杂因素的作用以及评估这种潜在偏倚的最佳敏感性分析。本文是药物流行病学特刊的一部分。
